Executive Summary: ISG Provider Lens™ Snowflake Ecosystem Partners - U.S. 2025

The individual quadrant reports are available at:

ISG Provider Lens™ Snowflake Ecosystem Partners - Snowflake Consulting and Advisory Services - U.S. 2025

ISG Provider Lens™ Snowflake Ecosystem Partners - Snowflake Implementation Services - U.S. 2025

ISG Provider Lens™ Snowflake Ecosystem Partners - Snowflake Managed and Support Services - U.S. 2025

A report comparing providers’ capabilities to help decision-makers more effectively source services

In the face of ongoing macroeconomic turbulence and uncertainty, U.S. firms are balancing a push for growth and business innovation initiatives with a heightened focus on risk management and resiliency. This approach is increasing demand for increased cloud-related spending aimed at improving business agility and responsiveness. It is also
driving initiatives to enable more effective data-driven decision-making and improve process visibility, predictability and resilience.

In 2025, Tech spending is primarily driven by investments in cybersecurity, infrastructure modernization and cloud adoption, data and analytics, and AI-enabled innovation. These priorities collectively accelerate demand for cloud data platforms such as Snowflake. The platforms enable firms to consolidate and manage diverse data types, including
structured and unstructured, into a single platform, eliminating data silos and improving data access, sharing and collaboration across the organization.

The US Snowflake ecosystem is impacted by a number of business priorities:

Enabling data-driven decision-making. While effectively managing data and improving data-driven decision-making have always been important business priorities, organizations now increasingly rely on data analytics to realize these goals. Growing concerns over global supply chain disruptions, heightened by uncertainty and tariff  threats, have driven projects focused on enhanced process automation, improved visibility and business continuity. Additionally, the surge in AI awareness, demand and adoption has dramatically increased the importance of data access, quality and management, driving significant changes in the data management landscape and increasing the demand for cloud data management platforms such as Snowflake and related services.

Enhancing data sharing, collaboration and monetization. Rising labor costs and tech skills shortages are driving initiatives to improve EX, productivity and retention. The constantly shifting dynamics of hybrid work have increased the demand for solutions and platforms that enable collaboration across geographical and organizational boundaries.
Cloud data platforms like Snowflake make data sharing and access easier for distributed teams. Providers differentiate themselves by helping organizations access data insights that improve employee productivity and scale nascent AI capabilities. Additionally, leading providers help firms leverage Snowflake’s data sharing capabilities and marketplace to create new revenue streams by monetizing data, transforming data into a tangible strategic asset.

Delivering insights for transparency, traceability and reporting. To operate effectively, U.S. firms must navigate a large number of state, federal and global data protection regulations, including CCPA, HIPAA, GLBA, COPPA and  GDPR. Organizations are also looking to consolidate and share data to improve their sustainability initiatives, improve
internal operations and manage extended supply chains to better measure and reduce carbon footprints. Given these complex requirements, AI-driven insights from a wellmanaged cloud data management platform are increasingly essential to address compliance challenges, operate effectively across jurisdictions, and achieve sustainability goals.

Ensuring effective data governance and security. Cybersecurity remains a top business priority and challenge, with concerns over data breaches and regulatory compliance putting the focus squarely on robust data governance frameworks and solutions. The growing focus on responsible and ethical AI has accelerated this trend, as increased AI usage necessitates improved data governance. One early benefit of the excitement surrounding GenAI initiatives is that organizations develop a clearer understanding of the importance of data governance and the need for a strong data foundation that supports not only structured but also semi-structured and unstructured data.

Organizations are implementing cloud data platforms to simplify the management of complex data environments through a unified platform. As these firms struggle with the challenges of data scale, security, privacy and accuracy, they will increasingly turn to service providers to understand the functionalities and implications of platforms like Snowflake in addressing their needs. They will also seek help in leveraging the Snowflake platform in production by implementing robust data governance and change management practices (for example, data lineage, access control and audit trails) at scale across hybrid or multicloud environments while adhering to varying regulatory standards across diverse regions and functions.

These business trends are among the key drivers of a changing technology and services landscape, directly impacting the U.S. Snowflake ecosystem. The following section outlines the key technology trends and their impact on the Snowflake ecosystem, especially highlighting the role of service providers.

Data scalability issues and performance bottlenecks. Traditional on-premises data warehouses often struggle to keep pace with the increasing volume, velocity and variety of modern enterprise data. As data grows exponentially, legacy systems become expensive and difficult to scale, resulting in performance degradation, slow query response times and resource contention, especially when supporting concurrent users or running AI and ML workloads. Data cloud platforms such as Snowflake offer elastic architectures that allow enterprises to scale compute and storage
independently, ensuring high performance without overprovisioning.

Real-time data processing and edge computing. Accessing, analyzing, visualizing and acting on data in real time is essential for maintaining a competitive edge, enhancing CX and supporting IoT devices, while edge computing reduces latency and bandwidth usage, complementing cloud data platforms. Snowflake supports real-time and near-realtime data ingestion pipelines and analysis, enabling timely data processing closer to data sources to enhance responsiveness and reduce latency.

Cloud migration and hybrid/multicloud complexity. Organizations are adopting hybrid and multicloud  approaches and leveraging multiple cloud services for flexibility, performance and redundancy. This includes  implementing multicloud Snowflake architectures across AWS, Azure and GCP to ensure resilience and cost  optimization. However, managing consistent data architecture, governance and performance across multiple cloud environments is inherently complex, especially for firms migrating from legacy, monolithic data platforms. The issues include data transfer latency, workload reconfiguration, compatibility issues and operational disruption risks. Snowflake simplifies this transition by offering a cloud-agnostic and fully managed architecture. Firms will look to service providers for guidance on leveraging Snowflake as a single, secure data platform across clouds to enable workload portability, regulatory compliance and business continuity without added infrastructure overhead.

Advanced analytics, AI and ML: The integration of advanced analytics, AI and ML into business operations requires robust, scalable data platforms. Legacy systems lack the capability to support modern analytics, AI and ML at scale. Enterprises need platforms that natively integrate with data science tools, support Python or R and allow for in-database ML processing. Firms need service providers’ guidance on best practices for leveraging data cloud platforms such as Snowflake, including the use of tools, accelerators and native integration with third-party ML platforms to prepare data, run models and deploy AI/GenAI solutions securely and efficiently.

Unified data management. The exponential growth of data from various sources requires advanced storage and processing capabilities. While cloud data platforms such as Snowflake are designed to manage large volumes of structured and unstructured data efficiently, migrating data from legacy systems to cloud platforms can be complex, costly and timeconsuming. Ensuring ongoing integration with existing applications and systems adds an additional layer of complexity. Leading providers excel in helping firms consolidate data lakes, warehouses and real-time  analytics into a single Snowflake platform to improve governance, scalability and operational efficiency. This includes implementing data fabric and data mesh architectures to enable more effective data integration and governance across diverse sources.

Data application development. Organizations are increasingly aware of the potential of developing and scaling data-intensive applications while minimizing operational burdens by leveraging a fully managed service such as Snowflake to handle infrastructure concerns such as provisioning, availability and maintenance. Firms with complex requirements are exploring opportunities to monetize their data through data sharing via Snowflake Marketplace. Regardless of the maturity level, firms are expected to increasingly seek providers’ guidance on leveraging  snowflake’s Native Application Framework to build, test and deploy applications directly within Snowflake, reducing data movement while maintaining tight security and governance.

Cloud cost management and optimization: Adopting a hybrid and/or multicloud approach can potentially reduce infrastructure costs. However, without careful management, cloud usage can become unpredictably expensive, especially concerning storage, compute and data egress charges. Additionally, many firms are not fully leveraging  their existing Snowflake investments, either failing to consume the credits they have already purchased or failing to adequately utilize the platform’s capabilities across various business functions and corporate domains. This scenario represents a significant opportunity for service providers to help firms optimize the costs of their Snowflake implementation.

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